Window Defect Sensing And Image Processing

ABSTRACT

Various embodiments relate to sensing defects associated with a window. Furthermore, various embodiments relate to performing image processing to produce a corrected image of a scene based at least partly on data corresponding to the detected defects. In some examples, one or more lighting modules may be used to illuminate the window to facilitate detection of the defects by one or more sensor devices.

BACKGROUND

This application is a Continuation of U.S. patent application Ser. No.17/000,134, filed Aug. 21, 2020, which is a divisional of U.S. patentapplication Ser. No. 15/980,637, filed May 15, 2018, now U.S. Pat. No.10,755,123, which claims benefit of priority to U.S. ProvisionalApplication Ser. No. 62/507,149, filed May 16, 2017, and which areincorporated herein by reference in their entirety.

TECHNICAL FIELD

This disclosure relates generally to systems, apparatus, and techniquesfor sensing defects associated with a window and for performing imageprocessing based at least partly on the sensed defects.

DESCRIPTION OF THE RELATED ART

Images of objects and/or scenes may be captured for various purposes.For instance, a camera may be used to capture images to obtaininformation about an environment. Sometimes obstructions interfere withimaging. As an example, a dirty window may be located within a field ofview of the camera and may negatively impact images captured by thecamera of a scene.

Motorized vehicles which are capable of sensing their environment andnavigating to destinations with little or no ongoing input fromoccupants, and may therefore be referred to as “autonomous” or“self-driving” vehicles, are an increasing focus of research anddevelopment. However, such vehicles typically include windows thatinterfere with the extent to which they are capable of sensing accuraterepresentations of their environment.

SUMMARY OF EMBODIMENTS

Various embodiments described herein relate to sensing/detecting defectsassociated with a window and performing image processing to produce acorrected image of a scene based at least partly on data correspondingto the detected defects.

In some embodiments, a system may include a window, one or multiplesensor devices, one or multiple lighting modules, and/or one or multipleprocessors. For instance, a first sensor device may be configured toimage at least a portion of the window (also referred to herein as the“window”). A second sensor device may be configured to image at least aportion of a scene (also referred to herein as the “scene”). In someinstances, the window may be located within the field of view of thesecond sensor device. As such, defects associated with the window mayinduce image altering effects on images obtained via the second sensordevice. For instance, surface defects on the window and/or volumedefects within the window may induce image altering effects such asshadowing, scattering, distortion, glint, etc.

The lighting module(s) may be configured to illuminate the window tofacilitate detection of the defects associated with the window. Forinstance, illumination of the window by the lighting module(s) may causethe defects to glow or otherwise act as secondary light sources, therebymaking the defects easier to detect by a sensor device. In someexamples, the lighting module(s) may include an edge lighting moduleand/or a graze lighting module. The edge lighting module may beconfigured to emit light, via one or multiple light sources, that isincident on at least one edge of the window. The graze lighting modulemay be configured to emit light, via one or multiple light sources, thatis incident on at least one side of the window.

In some examples, the processor(s) may be configured to receive signalscorresponding to images captured by the sensor devices, at least one ofwhich may include an altered representation of the scene based on theimage altering effects induced by the defects associated with thewindow. Furthermore, the processor(s) may perform image processing tocompensate for the image altering effects induced by the defects andproduce a corrected image of the scene.

In some examples, an individual sensor device may be configured withadaptive focus functionality such that the individual sensor is capableof adaptively switching between focusing on the window (to image thewindow) and focusing on the scene (to image the scene).

In some embodiments, a vehicle (e.g., an autonomous orpartially-autonomous vehicle) may include one or more components of thesystem described above. For instance, the vehicle may include a window(e.g., a windshield) that at least partially encompasses an interior ofthe vehicle. In various examples, the vehicle may include one ormultiple lighting modules configured to illuminate the window tofacilitate detection of defects associated with the window.

According to various embodiments, the vehicle may include an imagingsystem that includes one or multiple sensor devices that are configuredto perform imaging of objects. For instance, the imaging system may beconfigured to obtain data by imaging the window and/or a scene that isexterior to the vehicle.

Some embodiments include a method of detecting defects associated with awindow and/or performing image deconvolution based on defects associatedwith the window. In various embodiments, the method may include one ormore of the operations and components described above with respect tothe system and the vehicle.

In some embodiments, the method may include illuminating a window suchthat defects associated with the window are illuminated to facilitatedetection of the defects. For example, one or multiple lighting modules(e.g., the lighting modules described above with respect to the systemand the vehicle) may be used to illuminate the window. Furthermore, themethod may include imaging, via one or more sensor devices, the windowand a scene. For instance, a first sensor device may be used to imagethe window to obtain first data corresponding to the defects associatedwith the window. Imaging of the window may occur while the defects areilluminated by the lighting module(s). A second sensor device may beused to image the scene. The window (and its defects) may be locatedbetween the second sensor device and the scene. By imaging the scene,the second sensor device may obtain second data corresponding to analtered representation of the scene based at least in part on imagealtering effects induced by the defects.

In various implementations, the method may include deconvolving (e.g.,via one or more processors) the second data to produce a corrected imageof the scene. For instance, the second data (which may include thealtered representation of the scene) may be deconvolved based at leastin part on the first data (corresponding to the defects). To deconvolvethe second data, the processor(s) may perform image processing tocompensate for the image altering effects induced by the defects.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates a schematic diagram of an example system fordetecting defects associated with a window and/or performing imageprocessing to produce a corrected image of a scene based at least partlyon data corresponding to the detected defects, in accordance with someembodiments. The diagram of FIG. 1 includes a schematic perspective viewof the window and sensor device(s) of the system.

FIG. 2 illustrates a schematic diagram of another example system fordetecting defects associated with a window and/or performing imageprocessing to produce a corrected image of a scene based at least partlyon data corresponding to the detected defects, in accordance with someembodiments. The diagram of FIG. 2 includes a schematic side view of thewindow and sensor devices of the system, where the sensor devices are toa same side of the window.

FIG. 3 illustrates a schematic diagram of yet another example system fordetecting defects associated with a window and/or performing imageprocessing to produce a corrected image of a scene based at least partlyon data corresponding to the detected defects, in accordance with someembodiments. The diagram of FIG. 3 includes a schematic side view of thewindow and sensor devices of the system, where the sensor devices are toopposite sides of the window.

FIG. 4 illustrates a schematic diagram of still yet another examplesystem for detecting defects associated with a window and/or performingimage processing to produce a corrected image of a scene based at leastpartly on data corresponding to the detected defects, in accordance withsome embodiments. The diagram of FIG. 4 includes a schematic side viewof the window and an individual sensor device of the system.

FIG. 5 illustrates a schematic diagram of still yet another examplesystem for detecting defects associated with a window and/or performingimage processing to produce a corrected image of a scene based at leastpartly on data corresponding to the detected defects, in accordance withsome embodiments. The diagram of FIG. 5 includes a schematic side viewof the window and sensor devices of the system, where multiple sensordevices are individually configured to image a respective portion of thewindow.

FIGS. 6A-6E illustrate examples of window illumination using edgelighting modules for defect detection, in accordance with someembodiments. FIGS. 6A and 6B illustrate schematic front views of windowpanels and edge lighting modules. FIGS. 6C-6E each provide a schematictop view of a respectively illuminated window panel of the window panelsof FIGS. 6A and 6B.

FIG. 7 is a block diagram illustrating an example vehicle systemenvironment in which a control system uses multiple inputs to determinevehicle operations, in accordance with some embodiments. The inputs usedby the control system to determine vehicle operations may include inputsfrom an imaging system and/or an image deconvolver, in accordance withsome embodiments.

FIG. 8 is a flowchart of an example method of detecting defectsassociated with a window and/or performing image processing to produce acorrected image of a scene based at least partly on data correspondingto the detected defects, in accordance with some embodiments.

FIG. 9 is a flowchart of an example method of modifying a state ofoperation of an autonomous or partially-autonomous vehicle, inaccordance with some embodiments.

FIG. 10 is a flowchart of an example method of causing a window cleaningsystem to clean a window, in accordance with some embodiments.

FIG. 11 is a flowchart of an example method of designating a repairstatus and/or a replace status to a window, in accordance with someembodiments.

FIG. 12 is a block diagram illustrating an example computing device thatmay be used in at least some embodiments.

While embodiments are described herein by way of example for severalembodiments and illustrative drawings, those skilled in the art willrecognize that embodiments are not limited to the embodiments ordrawings described. It should be understood, that the drawings anddetailed description thereto are not intended to limit embodiments tothe particular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents and alternatives falling within thespirit and scope as defined by the appended claims. The headings usedherein are for organizational purposes only and are not meant to be usedto limit the scope of the description or the claims. As used throughoutthis application, the word “may” is used in a permissive sense (i.e.,meaning having the potential to), rather than the mandatory sense (i.e.,meaning must). Similarly, the words “include,” “including,” and“includes” mean including, but not limited to. When used in the claims,the term “or” is used as an inclusive or and not as an exclusive or. Forexample, the phrase “at least one of x, y, or z” means any one of x, y,and z, as well as any combination thereof.

DETAILED DESCRIPTION

Various embodiments described herein relate to sensing/detecting defectsassociated with a window and performing image processing to produce acorrected image of a scene based at least partly on data correspondingto the detected defects. In some cases, the defects associated with thewindow may interfere with imaging of the scene. For instance, the windowmay be located between the scene and a sensor device used to captureimages of the scene. As such, rather than providing an accuraterepresentation of the scene, the images of the scene may provide analtered representation of the scene, e.g., a representation of the scenethat is altered based at least in part on image altering effects causedby the defects.

According to various embodiments, to obtain an accurate representationof the scene, a corrected image may be produced by performing imageprocessing based at least partly on data corresponding to the windowdefects. A sensor device may be used to capture images of the window toobtain the data corresponding to the window defects. Furthermore, one ormultiple lighting modules may be used to illuminate the window tofacilitate detection of the defects by the sensor device.

In some embodiments, a system may include a window, one or multiplesensor devices, one or multiple lighting modules, and/or one or multipleprocessors. For instance, a first sensor device may be configured toimage at least a portion of the window (also referred to herein as the“window”). A second sensor device may be configured to image at least aportion of a scene (also referred to herein as the “scene”). In someinstances, the window may be located within the field of view of thesecond sensor device. As such, defects associated with the window mayinduce image altering effects on images obtained via the second sensordevice. For instance, surface defects on the window and/or volumedefects within the window may induce image altering effects such asshadowing, scattering, distortion, glint, etc.

The lighting module(s) may be configured to illuminate the window tofacilitate detection of the defects associated with the window. Forinstance, illumination of the window by the lighting module(s) may causethe defects to glow or otherwise act as secondary light sources, therebymaking the defects easier to detect by a sensor device. In someexamples, the lighting module(s) may include an edge lighting moduleand/or a graze lighting module. The edge lighting module may beconfigured to emit light, via one or multiple light sources, that isincident on at least one edge of the window. The graze lighting modulemay be configured to emit light, via one or multiple light sources, thatis incident on at least one side of the window.

In some examples, the processor(s) may be configured to receive signalscorresponding to images captured by the sensor devices, at least one ofwhich may include an altered representation of the scene based on theimage altering effects induced by the defects associated with thewindow. Furthermore, the processor(s) may perform image processing tocompensate for the image altering effects induced by the defects andproduce a corrected image of the scene.

For example, the processor(s) may receive a first set of one or moresignals corresponding to a first image (or multiple images) captured bythe first sensor device. For instance, the first sensor device maycapture the first image by imaging the window while the window isilluminated by the lighting module(s). The first set of signals mayinclude data corresponding to the defects associated with the window(also referred to herein as “defect data”).

Furthermore, the processor(s) may be configured to receive a second setof one or more signals corresponding to a second image (or multipleimages) captured by the second sensor device. For instance, the secondsensor device may capture the second image by imaging the scene. Thesecond set of signals and/or the second image may include an alteredrepresentation of the scene based at least in part on the image alteringeffects induced by the defects associated with the window.

In various embodiments, the processor(s) may be configured to deconvolvethe second set of signals to produce a corrected image of the scene. Forinstance, the second set of signals (which may include the alteredrepresentation of the scene) may be deconvolved based at least in parton the first set of signals (which may include the defect data). Todeconvolve the second set of signals, the processor(s) may perform imageprocessing to compensate for the image altering effects induced by thedefects.

According to some embodiments, an edge lighting module may include alight source (or multiple light sources) and a light guide. The lightguide may extend along at least a portion of an edge of the window.Furthermore, the light guide may be configured to direct light from thelight source to the window. In some cases, at least a portion of an edgelighting module may extend along a top edge of the window and may beconfigured to provide light in a downward direction through the window.Additionally, or alternatively, at least a portion of the edge lightingmodule may extend along a bottom edge of the window and may beconfigured to provide light in an upward direction through the window.

In some cases, the sensor device(s) may include a camera, a radardevice, and/or a light detection and ranging (LIDAR) device. In anon-limiting example, the first sensor device may be a first camera thatis focused on the window, and the second sensor device may be a secondcamera that is focused on the scene. However, in some embodiments, thesystem may include multiple different types of sensor devices.

Furthermore, in some examples, an individual sensor device may beconfigured with adaptive focus functionality such that the individualsensor is capable of adaptively switching between focusing on the window(to image the window) and focusing on the scene (to image the scene).

In some embodiments, a vehicle (e.g., an autonomous orpartially-autonomous vehicle) may include one or more components of thesystem described above. For instance, the vehicle may include a window(e.g., a windshield) that at least partially encompasses an interior ofthe vehicle. In various examples, the vehicle may include one ormultiple lighting modules configured to illuminate the window tofacilitate detection of defects associated with the window.

According to various embodiments, the vehicle may include an imagingsystem that includes one or multiple sensor devices that are configuredto perform imaging of objects. For instance, the imaging system may beconfigured obtain first data by imaging the window while the window isilluminated by the lighting module(s). The first data may include arepresentation of the defects associated with the window. Furthermore,the imaging system may be configured to obtain second data by imaging ascene that is exterior to the vehicle. The second data may include analtered representation of the scene based at least in part on imagealtering effects induced by the defects associated with the window.

In some examples, the vehicle may include one or multiple processorsconfigured to perform operations. For example, the operations mayinclude evaluating, based at least in part on the first data, one ormore parameters that characterize one or more defects associated withthe window to produce parameter evaluation data. The parameters mayinclude a distribution of the defects with respect to the window. Insome examples, the operations may include determining to modify a stateof operation of the vehicle based at least in part on the parameterevaluation data. In some embodiments, the operations may includedeconvolving the second data to produce a corrected image of the scene.For instance, the second data (which may include the alteredrepresentation of the scene) may be deconvolved based at least in parton the first data (which may include the representation of the defects).To deconvolve the second data, the processor(s) may perform imageprocessing to compensate for the image altering effects induced by thedefects.

In some embodiments, the imaging system may include a sensor device thatis configured to obtain both the first data and the second data. Forinstance, the sensor device may be configured with adaptive focusfunctionality that allows the sensor device to adaptively switchfocusing between the window (to image the window and obtain the firstdata) and focusing on the scene (to image the scene and obtain thesecond data). Additionally, or alternatively, the imaging system mayinclude a first sensor device configured to obtain the first data, and asecond sensor device configured to obtain the second data. For example,the first sensor device may be configured to focus on the window toimage the window and obtain the first data. The second sensor device maybe configured to focus on the scene to image the scene and obtain thesecond data. In various embodiments, the sensor device(s) of the imagingsystem may include a camera, a radar device, and/or a light detectionand ranging (LIDAR) device.

Furthermore, in some embodiments, the imaging system may includemultiple sensor devices that are individually configured to image andobtain data corresponding to a respective portion of multiple portionsof the window. For instance, a first sensor device be configured toimage and obtain data corresponding to a first portion of the window, asecond sensor device may be configured to image and obtain datacorresponding to a second portion of the window, etc. The processor(s)may be configured to deconvolve the second data (obtained by imaging thescene) to produce the corrected image based at least in part on the dataobtained by imaging multiple portions of the window.

In some cases, the processor(s) may be configured to determine to modifya state of operation of the vehicle based at least in part on the firstdata (obtained by imaging the window) and/or the corrected image.Additionally, or alternatively, the processor(s) may be configured todetermine to modify a state of operation of the vehicle based at leastin part on one or more degrees of confidence assigned to the first data(obtained by imaging the window), the second data (obtained by imagingthe scene), and/or the corrected image. The processor(s) may beconfigured to assign the one or more degrees of confidence to the firstdata, the second data, and/or the corrected image.

In some examples, the vehicle may include a window cleaning systemconfigured to spot clean the window. For instance, the processor(s) maybe configured to evaluate, based at least in part on the first data(obtained by imaging the window), one or more parameters thatcharacterize the defects associated with the window to produce parameterevaluation data. The parameters may include, for example, a distributionof the defects with respect to the window. The processor(s) maydetermine, based at least in part on the parameter evaluation data, tocause the window cleaning system to spot clean one or more particularareas of the window.

Additionally, or alternatively, the processor(s) may be configured todetermine to designate a repair status and/or a replace status to thewindow or one or more portions of the window. In some examples,designation of the repair status and/or the replace status may be basedat least in part on the first data obtained by imaging the window.Designation of the repair status may indicate a suggestion to repair atleast a portion of the window. Similarly, designation of the replacestatus may indicate a suggestion to replace at least a portion of thewindow.

Some embodiments include a method of detecting defects associated with awindow and/or performing image deconvolution based on defects associatedwith the window. In various embodiments, the method may include one ormore of the operations and components described above with respect tothe system and the vehicle.

In some embodiments, the method may include illuminating a window suchthat defects associated with the window are illuminated to facilitatedetection of the defects. For example, one or multiple lighting modules(e.g., the lighting modules described above with respect to the systemand the vehicle) may be used to illuminate the window. Furthermore, themethod may include imaging, via one or more sensor devices, the windowand a scene. For instance, a first sensor device may be used to imagethe window to obtain first data corresponding to the defects associatedwith the window. Imaging of the window may occur while the defects areilluminated by the lighting module(s). A second sensor device may beused to image the scene. The window (and its defects) may be locatedbetween the second sensor device and the scene. By imaging the scene,the second sensor device may obtain second data corresponding to analtered representation of the scene based at least in part on imagealtering effects induced by the defects.

In various implementations, the method may include deconvolving (e.g.,via one or more processors) the second data to produce a corrected imageof the scene. For instance, the second data (which may include thealtered representation of the scene) may be deconvolved based at leastin part on the first data (corresponding to the defects). To deconvolvethe second data, the processor(s) may perform image processing tocompensate for the image altering effects induced by the defects.

FIG. 1 illustrates a schematic diagram of an example system 100 fordetecting defects associated with a window and/or performing imageprocessing to produce a corrected image of a scene based at least partlyon data corresponding to the detected defects, in accordance with someembodiments. In various embodiments, the system 100 may include a window102, one or multiple sensor devices (e.g., first sensor device 104and/or second sensor device 106), one or multiple lighting modules(e.g., edge lighting module 108 and/or graze lighting module 110),and/or one or multiple processors (e.g., one or more processors of imagedeconvolver 112). In some embodiments, the system 100 may include one ormore multiple features, components, and/or operations of embodimentsdescribed herein with reference to FIGS. 2-12.

In some examples, the first sensor device 104 may be configured to imagethe window 102 to detect defects 116 associated with the window 102. Forinstance, the defects 116 may include surface defects (e.g., dustparticles on the window) and/or volume defects (e.g., cracks within thewindow). The second sensor device 106 may be configured to image a scene(or object) 106. In some cases, the window 102 may be located within thefield of view of the second sensor device 106. As such, the defects 116associated with the window 102 may induce image altering effects onimages obtained via the second sensor device 106. For instance, theimage altering effects may include shadowing, scattering, distortion,and/or glint. It should be understood, however, that the defects 116associated with the window 102 may cause other types of image alteringeffects.

The lighting module(s) may be configured to illuminate the window 102 tofacilitate detection of the defects 116 associated with the window 102.For instance, illumination of the window 102 by the lighting module(s)may cause the defects 116 to glow or otherwise act as secondary lightsources, thereby making the defects easier to detect by a sensor device(e.g., sensor device 104). In some examples, the lighting module(s) mayinclude an edge lighting module 108 and/or a graze lighting module 110.The edge lighting module 108 may be configured to emit light, via one ormultiple light sources, that is incident on at least one edge of thewindow (e.g., edge 118). The graze lighting module 110 may be configuredto emit light, via one or multiple light sources, that is incident on atleast one side of the window (e.g., side 120). For instance, the grazelighting module 110 may be configured to emit light that hits the side120 of the window at a non-zero angle.

In some examples, the image deconvolver 112 may be configured to receivedata and/or signals corresponding to images captured by the sensordevices 104, 106. For instance, the image deconvolver 112 may receive,as an input, first data 122 corresponding to one or more images capturedby the first sensor device 104. In some cases, the first sensor device104 may capture images by imaging the window 102 while the window 102 isilluminated by the edge lighting module and/or the graze lighting module110. The first data 122 may include data corresponding to the defects116 associated with the window 102. For example, the first data 122 mayinclude data indicating, with respect to the defects, one or more of:type, shape, size, chemistry, location, distribution, pattern, movement,etc.

In various embodiments, the image deconvolver 112 may be configured toreceive, as an input, second data 124 corresponding to one or moreimages captured by the second sensor device 106. In some cases, thesecond sensor device 106 may capture images by imaging the scene 114.The second data 124 may include an altered representation of the scene114 based at least in part on the image altering effects induced by thedefects 116 associated with the window 102.

As a non-limiting example, the scene 114 may include a road, a horizon,clouds, and the sun. The scene image in the block corresponding to thesecond data 124 may provide an example of an altered representation ofthe scene 114. As indicated in the scene image 124, the defects 116associated with the window 102 may induce image altering effects such asshadowing and/or scattering. For instance, notice that the bottomportion of the scene image 124 is darker than the top portion of thescene image 124.

In some examples, at least a portion of the scene image 124 may beuniformly altered and/or the alteration may have structure and impactdifferent pixels of a sensor (e.g., a sensor of the second sensor device106) in different manners. The alteration may depend on the type ofdefects 116 associated with the window 102, the type of sensor(s)/sensordevice(s) used to detect the defects 116, and/or the sensor arrangement(e.g., a sensor location relative to the window 102 and/or the scene114).

In various cases, the altered representation of the scene 114 may not bea desirable representation of the scene 114. For instance, in someexamples, the system 100 may be implemented in the context of anautonomous or partially-autonomous vehicle, and accurateimages/representations of the scene 114 may be desired for makingdecisions regarding navigation and/or other vehicle operations. In otherexamples, the system 100 may be implemented in other contexts in whichthe altered representation of the scene 114 may not be desirable.Accordingly, the image deconvolver 112 may be configured to deconvolvethe second data 124 to produce one or more corrected images 126 of thescene 114. For instance, a corrected image 126 of the scene 114 mayinclude an accurate representation of the scene 114, or at least arepresentation that is more accurate, with respect to the scene, thanthe altered representation provided by the second data 124.

In some embodiments, the image deconvolver 112 may be configured todeconvolve the second data 124 (which may include the alteredrepresentation of the scene 114) based at least in part on the firstdata 122 (which may include the data corresponding to the defects 116)to produce a corrected image 126 of the scene 114. To deconvolve thesecond data 124, the image deconvolver 112 may perform image processingto compensate for the image altering effects induced by the defects 116.In some examples, the image deconvolver 112 may use the first data 122to predict how defects 116 will alter the imaging of the scene 114. As anon-limiting example, the image deconvolver 112 may predict that dustparticles on the window 102 will induce a scattering effect on sceneimages 124 captured by the second sensor device 106. Accordingly, theimage deconvolver 112 may perform image processing to compensate for thescattering effect and/or any other image altering effect predicted bythe image deconvolver 112.

Referring back to the non-limiting example of the scene 114 discussedabove, the corrected image(s) produced by the image deconvolver 112 mayinclude an accurate representation of the road, horizon, clouds, and sunin the scene 114. As indicated by the image of the scene 114 in theblock corresponding to the corrected image 126, the image deconvolver114 may remove, reduce, or otherwise compensate for the image alteringeffects induced by the defects 116 associated with the window 102. Thus,the corrected image 126 may provide a clearer, higher quality, and/ormore accurate representation of the scene 114 than the scene image 124obtained via the second sensor device 106.

In some cases, the sensor device(s) may include a camera, a radardevice, and/or a light detection and ranging (LIDAR) device. In anon-limiting example, the first sensor device may be a first camera thatis focused on the window, and the second sensor device may be a secondcamera that is focused on the scene. However, in some embodiments, thesystem may include multiple different types of sensor devices.Furthermore, it should be understood that any other types of sensordevices suitable for imaging the window 102 and/or the scene 114 may beused in various embodiments.

As discussed in further detail below with reference to FIG. 4, in someembodiments an individual sensor device may be configured to adaptivelyswitch between imaging a window and imaging a scene. For instance, theindividual sensor device may be configured with adaptive focusfunctionality such that the individual sensor device is capable ofadaptively switching between focusing on the window (to image thewindow) and focusing on the scene (to image the scene).

FIG. 2 illustrates a schematic diagram of another example system 200 fordetecting defects associated with a window and/or performing imageprocessing to produce a corrected image of a scene based at least partlyon data corresponding to the detected defects, in accordance with someembodiments. In some embodiments, the system 200 may include one or moremultiple features, components, and/or operations of embodimentsdescribed herein with reference to FIGS. 1 and 3-12. For instance, thesystem 200 may include the window 102, one or multiple sensor devices(e.g., the first sensor device 104 and/or the second sensor device 106),one or multiple lighting modules 202 (e.g., edge lighting module 108and/or graze lighting module 110), and/or one or multiple processors(e.g., one or more processors of image deconvolver 112).

In some embodiments, the lighting module(s) 202 may include one ormultiple edge lighting modules that are individually configured to emitlight that is incident on at least one respective edge of the window102. For instance, the lighting module(s) 202 may include a top edgelighting module that may extend along a top edge of the window 102. Thetop edge lighting module may be configured to provide light in adownward direction through the window 102, as indicated by arrow 204.Additionally, or alternatively, the lighting module(s) 202 may include abottom edge lighting module that may extend along a bottom edge of thewindow 102. The bottom edge lighting module may be configured to providelight in an upward direction through the window 102, as indicated byarrow 206. In other embodiments, the lighting module(s) may include oneor more edge lighting modules that are configured to provide light viaother edges of the window 102 (e.g., in directions orthogonal to theside view of the window illustrated in FIG. 2).

In some examples, the lighting module(s) 202 may include one or multiplegraze lighting modules that are individually configured to emit lightthat is incident on at least one respective side of the window 102. Forinstance, the lighting module(s) 202 may include a first set of one ormore graze lighting modules that are configured to emit light that isincident on a first side of the window, e.g., in the directionsindicated by arrows 208 and 210. Additionally, or alternatively, thelighting module(s) 202 may include a second set of one or more grazelighting modules that are configured to emit light that is incident on asecond side of the window, e.g., in the directions indicated by arrows212 and 214. In various embodiments, light emitted by a graze lightingmodule may hit a side of the window 102 at an angle or at multipledifferent angles. For instance, the graze lighting module (or acombination of multiple graze lighting modules) may include differentsets of light with different angles of illumination incident on thewindow 102.

As illustrated in FIG. 2, in various embodiments the sensor devices 104,106 may be to a same side of the window 102. The scene 216 may be to anopposite side of the window relative to the sensor device 104, 106. Thatis, the window 102 may be located between the sensor devices 104, 106and the scene 216. However, in other embodiments, one or more sensordevices may be located to a same side of the window 102 as the scene216, e.g., as shown in FIG. 3. As discussed above with reference to FIG.1, the first sensor device 104 may be configured to image the window 102to detect defects associated with the window 102. The second sensordevice 106 may be configured to image the scene 216.

In various examples, the image deconvolver 112 may be configured toreceive, as input, data and/or signals corresponding to images capturedby the sensor devices 104, 106. For instance, the image deconvolver 112may receive first data 218 from the first sensor device 104, and seconddata 220 from the second sensor device 106. The first data 218 mayinclude data corresponding to defects associated with the window 102.The second data 220 may include an altered representation of the scene216 based at least in part on image altering effects induced by thedefects associated with the window 102. The image deconvolver 112 may beconfigured to deconvolve the second data 220 based at least in part onthe first data 218 to produce a corrected image 222 of the scene 216,e.g., as discussed above with reference to FIG. 1.

FIG. 3 illustrates a schematic diagram of yet another example system 300for detecting defects associated with a window and/or performing imageprocessing to produce a corrected image of a scene based at least partlyon data corresponding to the detected defects, in accordance with someembodiments. In some embodiments, the system 300 may include one or moremultiple features, components, and/or operations of embodimentsdescribed herein with reference to FIGS. 1, 2, and 4-12. For instance,the system 300 may include the window 102, one or multiple sensordevices (e.g., the first sensor device 104 and/or the second sensordevice 106), one or multiple lighting modules 202 (e.g., edge lightingmodule 108 and/or graze lighting module 110), and/or one or multipleprocessors (e.g., one or more processors of image deconvolver 112).

As illustrated in FIG. 3, in various embodiments the sensor devices 104,106 may be to opposite sides of the window 102. The first sensor device104 may be to a same side of the window 102 as the scene 302, and thesecond sensor device 106 may be to an opposing side of the window 102.That is, the window 102 may be disposed between the first sensor device104 and the second sensor device 106. As discussed above with referenceto FIG. 1, the first sensor device 104 may be configured to image thewindow 102 to detect defects associated with the window 102. The secondsensor device 106 may be configured to image the scene 216.

In various examples, the image deconvolver 112 may be configured toreceive, as input, data and/or signals corresponding to images capturedby the sensor devices 104, 106. For instance, the image deconvolver 112may receive first data 304 from the first sensor device 104, and seconddata 306 from the second sensor device 106. The first data 304 mayinclude data corresponding to defects associated with the window 102.The second data 306 may include an altered representation of the scene302 based at least in part on image altering effects induced by thedefects associated with the window 102. The image deconvolver 112 may beconfigured to deconvolve the second data 306 based at least in part onthe first data 304 to produce a corrected image 308 of the scene 302,e.g., as discussed above with reference to FIG. 1.

FIG. 4 illustrates a schematic diagram of still yet another examplesystem 400 for detecting defects associated with a window and/orperforming image processing to produce a corrected image of a scenebased at least partly on data corresponding to the detected defects, inaccordance with some embodiments. In some embodiments, the system 400may include one or more multiple features, components, and/or operationsof embodiments described herein with reference to FIGS. 1-3 and 5-12.The system 400 may include the window 102, a sensor device 402, one ormultiple lighting modules 202 (e.g., edge lighting module 108 and/orgraze lighting module 110), and/or one or multiple processors (e.g., oneor more processors of image deconvolver 112).

As illustrated in FIG. 4, in various embodiments the system 400 mayinclude an individual sensor device 402 that is configured to image thewindow 102 to detect defects (e.g., as performed by the first sensordevice 104 described above with reference to FIGS. 1-3), and to imagethe scene 404 (e.g., as performed by the second sensor device 106described above with reference to FIGS. 1-3). In some examples, thesensor device 402 may be configured with adaptive focus functionalitysuch that it is capable of adaptively switching between focusing on thewindow 102 (to image the window) and focusing on the scene 404 (to imagethe scene).

In various examples, the image deconvolver 112 may be configured toreceive, as input, data and/or signals corresponding to images capturedby the sensor device 402. For instance, the image deconvolver 112 mayreceive, from the sensor device 402, first data 406 that includes datacorresponding to defects associated with the window 102. Furthermore,the image deconvolver 112 may receive, from the sensor device 402,second data 408 that includes an altered representation of the scene 404based at least in part on image altering effects induced by the defectsassociated with the window 102. The image deconvolver 112 may beconfigured to deconvolve the second data 408 based at least in part onthe first data 406 to produce a corrected image 410 of the scene 404,e.g., as discussed above with reference to FIG. 1.

FIG. 5 illustrates a schematic diagram of still yet another examplesystem 500 for detecting defects associated with a window and/orperforming image processing to produce a corrected image of a scenebased at least partly on data corresponding to the detected defects, inaccordance with some embodiments. In some embodiments, the system 500may include one or more multiple features, components, and/or operationsof embodiments described herein with reference to FIGS. 1-4 and 6-12.The system 500 may include the window 102, an imaging system 502, one ormultiple lighting modules 202 (e.g., edge lighting module 108 and/orgraze lighting module 110), and/or one or multiple processors (e.g., oneor more processors of image deconvolver 112).

As illustrated in FIG. 5, in various embodiments the imaging system 502may include sensor devices that individually image a respective portionof the window 102. For instance, a first sensor device 504 may be usedto image a first portion 506 of the window 102, and a second sensordevice 508 may be used to image a second portion 510 of the window 102.The second portion 510 of the window 102 may be different than the firstportion 506 of the window 102. Although FIG. 5 depicts two sensordevices 504, 508 for imaging respective portions of the window, itshould be understood that a different number of sensor devices may beused to image respective portions of the window in some embodiments.

In some examples, the imaging system 502 may include a third sensordevice 512 for imaging the scene 514. In some embodiments, the window102 may be disposed between sensor devices of the imaging system 502 andthe scene 514. For instance, the window 102 may be located within afield of view of the third sensor device 512 that is configured to imagethe scene 514. Although FIG. 5 depicts a single sensor device (thirdsensor device 512 for imaging the scene 514, it should be understoodthat multiple sensor devices may be used to image the scene in someembodiments.

In various examples, the image deconvolver 112 may be configured toreceive, as input, data and/or signals corresponding to images capturedby the sensor devices 504, 508, and 512. For instance, the imagedeconvolver 112 may receive first data 516 from the first sensor device504, second data 518 from the second sensor device 508, and third data520 from the third sensor device 512. The first data 516 may includedata corresponding to defects associated with the first portion 506 ofthe window 102. The second data 518 may include data corresponding todefects associated with the second portion 510 of the window 102. Thethird data 520 may include an altered representation of the scene 514based at least in part on image altering effects induced by the defectsassociated with the window 102. The image deconvolver 112 may beconfigured to deconvolve the third data 520 based at least in part on atleast one of the first data 516 or the second data 518 to produce acorrected image 522 of the scene 514, e.g., as discussed above withreference to FIG. 1.

FIGS. 6A-6E illustrate examples of window illumination using edgelighting modules for defect detection, in accordance with someembodiments. FIG. 6A and 6B illustrate schematic front views of windowpanels and edge lighting modules. FIGS. 6C-6E each provide a schematictop view of a respectively illuminated window panel of the window panelsof FIGS. 6A and 6B. In some embodiments, the examples 600 a-600 e mayinclude one or more multiple features, components, and/or operations ofembodiments described herein with reference to FIGS. 1-5 and 7-12.

Examples 600 a (illustrated in FIG. 6A) and 600 b (illustrated in FIG.6B) show window panels that are coupled to edge lighting modules. Inexample 600 a, the window panels are in a first state where they are notbeing illuminated by the edge lighting modules. In example 600 b, thewindow panels are in a second state where they are being illuminated bythe edge lighting modules.

The window panels 602 a, 604 a, 606 a are coupled to a top edge lightingmodule 608 a and a bottom edge lighting module 610 a. The first windowpanel 602 a is substantially without surface or volume defects. Thesecond window panel 604 a includes volume defects (not visible in FIG.6A). The third window panel 606 a includes surface defects 612 a. Aswill be discussed in further detail below with reference to FIGS. 6C-6E,the edge lighting modules 608 a, 610 a may each include one or morelight sources and one or more light guides. The light guides may beconfigured to direct light from the light source to the window.

In example 600 b (illustrated in FIG. 6B), the window panels 602 a-606 aare being illuminated by the edge lighting modules 608 a, 610 a.Differences between the effects of the illumination on the respectivewindow panels are evident. The first window panel 602 a, which issubstantially free of surface or volume defects, does not substantiallychange in appearance as a result of the illumination in example 600 b ascompared to its appearance in example 600 a (illustrated in FIG. 6A).The second panel 604 a, which includes volume defects, glowssubstantially throughout as a result of the illumination causing thevolume defects to act as secondary light sources, thus facilitatingdetection of the volume defects. The third panel 606 a, which includessurface defects, glows along an outer rectangular portion correspondingto where the surface defects are located, as a result of theillumination causing the surface defects to act as secondary lightsources, thus facilitating detection of the surface defects.

Example 600 c in FIG. 6C shows a schematic top view of the first windowpanel 602 a and a portion of the top edge lighting module 608 a, wherethe first window panel 602 a is being illuminated by the top edgelighting module 608 a and/or the bottom edge lighting module 610 a. Thetop edge lighting module 608 a may include a light source 602 c (e.g., alight-emitting diode (LED)) and a light guide 604 c (e.g., a light guideplate). In some examples, the light source 602 c may be coupled (e.g.,via one or more wires) to a power source (not shown) configured toprovide electrical power to the light source 602 c. The light guide 604c may be configured to direct light from the light source 602 c to atleast a portion of the first window panel 602 a. For instance, the lightguide 604 c may extend along a top edge of the first window panel 602 aand may be configured to provide light in a downward direction throughthe first window panel 602 a. Furthermore, the light guide 604 c may beconfigured to direct light from the light source 602 c in one or moreother directions, e.g., the directions indicated by the light rays 606 cin example 600 c. Because the first window panel 602 a is substantiallywithout surface or volume defects, example 600 c does not indicate anydefects acting as secondary light sources as a result of illumination ofthe first window panel 602 a by the top edge lighting module 608 aand/or the bottom edge lighting module 610 a.

Example 600 d in FIG. 6D shows a schematic top view of the second windowpanel 604 a and a portion of the top edge lighting module 608 a, wherethe second window panel 604 a is being illuminated by the top edgelighting module 608 a and/or the bottom edge lighting module 610 a.Because the second window panel 604 a includes volume defects 602 d,example 600 d indicates the volume defects 602 d acting as secondarylight sources as a result of illumination of the second window panel 604a by the top edge lighting module 608 a and/or the bottom edge lightingmodule 610 a. For example, the volume defects 602 d, when acting assecondary light sources, may direct light in directions indicated by thelight rays 604 e in example 600 d. In this manner, the volume defects602 d may be easier to detect, e.g., by sensor devices such as thosedescribed herein with respect to FIGS. 1-5 and 7-12.

Example 600 e in FIG. 6E shows a schematic top view of the third windowpanel 606 a and a portion of the top edge lighting module 608 a, wherethe third window panel 606 a is being illuminated by the top edgelighting module 608 a and/or the bottom edge lighting module 610 a.Because the third window panel 606 a includes surface defects 612 a,example 600 e indicates the surface defects 612 a acting as secondarylight sources as a result of illumination of the third window panel 606a by the top edge lighting module 608 a and/or the bottom edge lightingmodule 610 a. For example, the surface defects 612 a, when acting assecondary light sources, may direct light in directions indicated by thelight rays 602 e in example 600 e. In this manner, the surface defects612 a may be easier to detect, e.g., by sensor devices such as thosedescribed herein with respect to FIGS. 1-5 and 7-12.

FIG. 7 is a block diagram illustrating an example vehicle systemenvironment 700 in which a control system uses multiple inputs todetermine vehicle operations, in accordance with some embodiments. Asdiscussed below, the inputs used by the control system to determinevehicle operations may include inputs from an imaging system and/or animage deconvolver, in accordance with some embodiments. In someembodiments, the vehicle system environment 700 may include one or moremultiple features, components, and/or operations of embodimentsdescribed herein with reference to FIGS. 1-6 and 8-12.

According to various examples, the vehicle system environment 700 mayinclude a vehicle 702 (e.g., an autonomous or partially-autonomousvehicle) and a control system 704 configured to control vehicleoperations 706. For instance, the control system 704 may include one ormore controllers and/or processors. In some examples, the control system704 may be part of the vehicle 702. Furthermore, the vehicle 702 mayinclude an imaging system 708 (e.g., the imaging systems and/or sensordevices described herein with reference to FIGS. 1-6E and 8-12) and/oran image deconvolver 710 (e.g., the image deconvolvers described hereinwith reference to FIGS. 1-6E and 8-12). In some cases, the controlsystem 704 and/or the image deconvolver 710 may be separate and/orremote from the vehicle 702.

In some embodiments, the vehicle 702 may include one or more windows(e.g., the windows described herein with reference to FIGS. 1-6E and8-11). For instance, the window(s) may at least partially encompass aninterior of the vehicle 702. Furthermore, the vehicle 702 may includeone or more lighting modules (e.g., the lighting modules describedherein with respect to FIGS. 1-6E and 8-11) configured to illuminate thewindow(s) to facilitate detection of defects associated with thewindow(s). The imaging system 708 may include one or more sensor devicesconfigured to perform imaging of objects, such as the window(s) and oneor more scenes (e.g., a scene that is exterior to the vehicle 702).

In various examples, the image deconvolver 710 may be configured toreceive, as input, data and/or signals corresponding to images obtainedvia the imaging system 708. For instance, the image deconvolver 710 mayreceive first data obtained via imaging of a window by the imagingsystem 708. The first data may include data corresponding to defectsassociated with a window of the vehicle 702. Furthermore, the imagedeconvolver 710 may receive second data obtained via imaging of a sceneby the imaging system 708. The second data may include an alteredrepresentation of the scene based at least in part on image alteringeffects induced by the defects associated with the window. The imagedeconvolver 710 may be configured to deconvolve the second data based atleast in part on at least one of the first data to produce a correctedimage of the scene, e.g., as discussed above with reference to FIG. 1.

According to various embodiments, the control system 704 may receive, asinputs, data from the imaging system 708 and/or the image deconvolver710. Additionally, or alternatively, the control system 704 may receiveuser inputs 712 and/or other inputs 714. For instance, user inputs 712may include inputs to a user interface of the vehicle 702, such asinputs corresponding to vehicle operations 706 that the user desires toimplement. Other inputs 714 may include, for example, data from othersensors of the vehicle 702, time data, weather data, calendar data, datafrom other vehicles (e.g., location and/or motion data associated withother vehicles), historical data, etc. In some instances, the otherinputs 714 may be obtained from one or more sources that are external tothe vehicle 702, for example, via wireless communication over one ormore networks.

In some implementations, the control system 704 may include decisionmaking components configured to make determinations with respect tovarious aspects of vehicle operations 706. For instance, the controlsystem 704 may be configured to make motion-related decisions, such aswhether to accelerate, slow down, change lanes, etc. Furthermore, thecontrol system 704 may be configured to control various aspects ofvehicle operations 706. For instance, the control system 704 may sendinstructions to various components of the vehicle 702 to control thevehicle operations 706 (which may include, for example, operations ofthe imaging system 708 and/or the image deconvolver 710). In someembodiments, the control system 706 may be configured to make decisionswith respect to utilization of data received from the imaging system 708and/or the image deconvolver 710. In some instances, e.g., if there isredundancy with other sensors in the vehicle 702, the control system 706may determine to exclude one or more particular portions of the windowfrom image analysis and/or image correction. In some cases, the controlsystem 706 may instruct the image deconvolver 710 to not perform imagecorrection with respect to data corresponding to one or more particularportions of the window. In some embodiments, the portion(s) of thewindow that are to be excluded from image analysis and/or imagecorrection may be determined based at least in part on data from theimaging system 708 and/or the image deconvolver 710, degrees ofconfidence assigned to data received from the imaging system 708 and/orthe image deconvolver 710 (discussed below), parameter evaluation data(discussed below), user inputs 712, and/or the other inputs 714.

According to some embodiments, the control system 704 may be configuredto determine to modify a state of operation of the vehicle 702 based atleast in part on data received from the imaging system 708 and/or theimage deconvolver 710. For instance, the control system 704 may modify astate of the vehicle operations 706 based at least in part on datacorresponding to defects associated with the window(s) and/or datacorresponding to corrected images produced by the image deconvolver 710.

In some cases, the control system 704 may be configured to assign one ormore degrees of confidence to data received from the imaging system 708and/or the image deconvolver 710, as described below with reference toFIG. 9. As a non-limiting example, a respective degree of confidence maybe assigned to each of: data corresponding to defects associated withthe window, data corresponding to a representation of the scene, and/orcorrected images produced by the image deconvolver 710. The respectivedegrees of confidence may be compared to one or more confidence levelthresholds (e.g., a low confidence threshold, a moderate confidencethreshold, a high confidence threshold, etc.). Based at least in part onthe degrees of confidence and/or a comparison of the degrees ofconfidence to a confidence level threshold, the control system 704 maydetermine whether to modify a state of the vehicle operations 706. Forinstance, in making decisions, the control system 704 may disregard datathat is determined to correspond to a particular confidence level (e.g.,a low confidence level). Furthermore, in making decisions, the controlsystem 704 may assign a higher priority to data that is determined tocorrespond to a particular confidence level (e.g., a high confidencelevel) and/or may assign a lower priority to data that is determined tocorrespond to a lower confidence level (e.g., a lower confidence level).

In some embodiments, the control system 704 may be configured to makedecisions with respect to a window cleaning system of the vehicle 702,as described below with reference to FIG. 10. The window cleaning systemmay be configured to clean the window(s). For instance, the windowcleaning system may be configured to perform spot cleaning. That is, thewindow cleaning system may clean particular portions, or spots, of thewindow(s). In some examples, the window cleaning system may include oneor more window wiping components, one or more window cleaning fluids(e.g., water, a solution, etc.), and/or one or more fluid dispensers.

The control system 704 may be configured to evaluate one or moreparameters that characterize the defects associated with the window(s)to produce parameter evaluation data. For example, the parameters thatcharacterize the defects may include a distribution of the defects withrespect to the window. Other parameters that characterize the defectsmay include type, shape, size, chemistry, location, pattern, movement,etc., of the defects. In some implementations, the control system 704may evaluate the parameters based at least in part on data received fromthe imaging system 708 and/or the image deconvolver 710, such as datacorresponding to defects associated with the window(s) obtained via theimaging system 708. As a non-limiting example, the control system 704may use the parameter evaluation data to determine that a first portionof a window should be spot cleaned and to determine that a secondportion of the window should not be spot cleaned, and thus the controlsystem 704 may instruct the window cleaning system to spot clean thefirst portion of the window but not spot clean the second portion of thewindow.

Additionally, or alternatively, the control system 704 may be configuredto determine whether to designate a repair status and/or a replacestatus to a window (or a portion of the window), as described below withreference to FIG. 11. For instance, as discussed above, the controlsystem 704 may evaluate the parameters that characterize the defects toproduce parameter evaluation data. The control system 704 may use theparameter evaluation data to make the decision of whether to designate arepair status and/or a replace status to the window. Designation of arepair status may indicate a suggestion to repair at least a portion ofthe window. Likewise, designation of a replace status may indicate asuggestion to replace at least a portion of the window. Such suggestionsmay be provided via an audible and/or a visible output. For example, thevehicle 702 may include an electronic display, and the control system704 may cause the electronic display to present a user interface thatprovides a visible suggestion to repair and/or replace the window.Additionally, or alternatively, the vehicle 702 may include one or morespeakers, and the control system 704 may cause the speakers to presentan audible suggestion to repair and/or replace the window.

FIG. 8 is a flowchart of an example method 800 of detecting defectsassociated with a window and/or performing image processing to produce acorrected image of a scene based at least partly on data correspondingto the detected defects, in accordance with some embodiments. In someembodiments, the method 800 may include one or more multiple features,components, and/or operations of embodiments described herein withreference to FIGS. 1-7 and 9-12.

At 802, the method 800 may include illuminating a window via one or morelighting modules to facilitate detection of defects associated with thewindow. For instance, as described above with reference to FIGS. 1-7,the lighting modules may include one or multiple edge lighting modulesand/or one or multiple graze lighting modules.

At 804, the method 800 may include imaging the window to obtain firstdata corresponding to the defects associated with the window. Forinstance, as described above with reference to FIGS. 1-7, one or moresensor devices (e.g., a sensor device of an imaging system) may be usedto image the window. At 806, the method 800 may include imaging a sceneto obtain second data corresponding to an altered representation of thescene based at least in part on image altering effects induced by thedefects associated with the window. For instance, as described abovewith reference to FIGS. 1-7, one or more sensor devices (e.g., a sensordevice) may be used to image the scene. In some embodiments, the sensordevice used to image the window may also be used to image the scene. Inother embodiments, a sensor device may be used to image the window andanother sensor device may be used to image the scene.

At 808, the method 800 may include deconvolving, based at least in parton the first data, the second data to produce a corrected image of thescene. For example, to deconvolve the second data, an image deconvolvermay perform image processing to compensate for the image alteringeffects induced by the defects.

As indicated in FIG. 8, in some embodiments, the method 800 may includeone or more of the operations discussed below with reference to FIGS.9-11.

FIG. 9 is a flowchart of an example method 900 of modifying a state ofoperation of a vehicle (e.g., an autonomous or partially-autonomousvehicle), in accordance with some embodiments. In some embodiments, themethod 900 may include one or more multiple features, components, and/oroperations of embodiments described herein with reference to FIGS. 1-8and 10-12.

At 902, the method 900 may include assigning degrees of confidence tofirst data, second data, and/or a corrected image (e.g., the first data,the second data, and/or the corrected image of the method 800 discussedabove with reference to FIG. 8). For instance, as described above withreference to FIG. 7, a control system of a vehicle may be configured toassign one or more degrees of confidence to data received from theimaging system and/or the image deconvolver. In some cases, the degreesof confidence may be compared to one or more confidence levelthresholds. At 904, the method 900 may include modifying and/ordetermining to modify a state of operation of the vehicle. For instance,as described above with reference to FIG. 7, the control system of thevehicle may determine to modify a state of operation of the vehiclebased at least in part on: data received from the imaging system and/orthe image deconvolver, degrees of confidence assigned to the datareceived from the imaging system and/or the image deconvolver, and/or acomparison of one or more of the degrees of confidence to one or moreconfidence level thresholds.

FIG. 10 is a flowchart of an example method 1000 of causing a windowcleaning system to clean a window, in accordance with some embodiments.In some embodiments, the method 1000 may include one or more multiplefeatures, components, and/or operations of embodiments described hereinwith reference to FIGS. 1-9 and 10-12.

At 1002, the method 1000 may include evaluating parameters thatcharacterize defects associated with the window. For instance, theparameters may be evaluated based at least in part on the first data ofthe method 800 discussed above with reference to FIG. 8. As describedabove with reference to FIG. 7, in some embodiments a control system ofa vehicle may be configured to evaluate the parameters to produceparameter evaluation data. In some instances, the parameters thatcharacterize the defects may include a distribution of the defects withrespect to the window. Other parameters that characterize the defectsmay include type, shape, size, chemistry, location, pattern, movement,etc., of the defects.

At 1004, the method 1000 may include causing a window cleaning system toclean the window. For instance, as described above with reference toFIG. 7, a control system may be configured to cause the window cleaningsystem to clean part (e.g., particular spots) or the entire window. Insome examples, the window cleaning system may include one or more windowwiping components, one or more window cleaning fluids (e.g., water, asolution, etc.), and/or one or more fluid dispensers. As a non-limitingexample, the parameter evaluation data may be used to determine that afirst portion of a window should be spot cleaned and to determine that asecond portion of the window should not be spot cleaned. Based at leastin part on such a determination, the window cleaning system may beinstructed to spot clean the first portion of the window but not spotclean the second portion of the window. In some examples, instructionsmay cause at least a portion of the window cleaning system to moveproximate to, and/or aim at, one or more portions of the window that areto be cleaned. For instance, a window cleaning component may be moved toa location proximate a portion of the window such that the windowcleaning component is capable of cleaning the portion of the window.Additionally, or alternatively, a fluid dispenser may be aimed at theportion of the window such that the fluid dispenser is capable ofdispensing a window cleaning fluid at the portion of the window.

FIG. 11 is a flowchart of an example method 1100 of designating a repairstatus and/or a replace status to a window, in accordance with someembodiments. In some embodiments, the method 1100 may include one ormore multiple features, components, and/or operations of embodimentsdescribed herein with reference to FIGS. 1-10 and 12.

At 1102, the method 1100 may include evaluating parameters thatcharacterize defects associated with the window. For instance, theparameters may be evaluated based at least in part on the first data ofthe method 800 discussed above with reference to FIG. 8. As describedabove with reference to FIG. 7, in some embodiments a control system ofa vehicle may be configured to evaluate the parameters to produceparameter evaluation data. In some instances, the parameters thatcharacterize the defects may include a distribution of the defects withrespect to the window. Other parameters that characterize the defectsmay include type, shape, size, chemistry, location, pattern, movement,etc., of the defects.

At 1104, the method 1100 may include determining whether the windowshould be repaired. If it is determined, at 1104, that the window shouldbe repaired, then the method 1100 may include designating a repairstatus to the window, at 1106. If, on the other hand, it is determined,at 1104, that the window should not be repaired, then the method 1100may include determining whether the window should be replaced, at 1108.If it is determined, at 1108, that the window should be replaced, thenthe method 1100 may include designating a replace status to the window.If, on the other hand, it is determined, at 1108, that the window shouldnot be replaced, then the method 1000 may continue to evaluate theparameters that characterize defects associated with the window, at1102. In various embodiments, evaluation of the parameters may occurcontinuously, periodically, and/or in response to an event and/or atrigger (e.g., after a repair status and/or a replace statusdesignation, in response to a user request, etc.).

As described above with reference to FIG. 7, in various embodimentsparameter evaluation data may be used to make the decision of whether todesignate a repair status and/or a replace status. Designation of arepair status may indicate a suggestion to repair at least a portion ofthe window. Likewise, designation of a replace status may indicate asuggestion to replace at least a portion of the window.

FIG. 12 is a block diagram illustrating an example computing device 1200that may be used in at least some embodiments. In some embodiments, thecomputing device 1200 may implement a portion or all of one or more ofthe operations described herein with reference to FIGS. 1-11. In someexamples, the computing device 1200 may include a general-purposecomputing system that includes or is configured to access one or morecomputer-accessible media.

In some embodiments, the computing device 1200 may include one or moreprocessors 1202 coupled to a main memory 1204 (which may comprise bothnon-volatile and volatile memory modules, and may also be referred to assystem memory) via an input/output (I/O) interface 1206. Computingdevice 1200 may further include a network interface 1208 coupled to I/Ointerface 1206, as well as additional I/O devices 1210 which may includesensors of various types.

In various embodiments, computing device 1200 may be a uniprocessorsystem including one processor 1202, or a multiprocessor systemincluding several processors 1202 (e.g., two, four, eight, or anothersuitable number). Processors 1202 may be any suitable processors capableof executing instructions. For example, in various embodiments,processors 1202 may be general-purpose or embedded processorsimplementing any of a variety of instruction set architectures (ISAs),such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitableISA. In multiprocessor systems, each of processors 1202 may commonly,but not necessarily, implement the same ISA. In some implementations,graphics processing units (GPUs) may be used instead of, or in additionto, conventional processors.

Memory 1204 may be configured to store instructions and data accessibleby processor(s) 1202. In at least some embodiments, the memory 1204 maycomprise both volatile and non-volatile portions; in other embodiments,only volatile memory may be used. In various embodiments, the volatileportion of system memory 1204 may be implemented using any suitablememory technology, such as static random access memory (SRAM),synchronous dynamic RAM or any other type of memory. For thenon-volatile portion of system memory (which may comprise one or moreNVDIMMs, for example), in some embodiments flash-based memory devices,including NAND-flash devices, may be used. In at least some embodiments,the non-volatile portion of the system memory may include a powersource, such as a supercapacitor or other power storage device (e.g., abattery). In various embodiments, memristor based resistive randomaccess memory (ReRAM), three-dimensional NAND technologies,Ferroelectric RAM, magnetoresistive RAM (MRAM), or any of various typesof phase change memory (PCM) may be used at least for the non-volatileportion of system memory. In the illustrated embodiment, executableprogram instructions 1212 and data 1214 implementing one or more desiredfunctions, such as those methods, techniques, and data described abovewith reference to FIGS. 1-11, are shown stored within main memory 1204.

In some embodiments, I/O interface 1206 may be configured to coordinateI/O traffic between processor 1202, main memory 1204, and variousperipheral devices, including network interface 1208 or other peripheralinterfaces such as various types of persistent and/or volatile storagedevices, sensor devices, etc. In some examples, I/O interface 1206 mayperform any necessary protocol, timing or other data transformations toconvert data signals from one component (e.g., main memory 1204) into aformat suitable for use by another component (e.g., processor 1202). Insome embodiments, I/O interface 1206 may include support for devicesattached through various types of peripheral buses, such as a variant ofthe Peripheral Component Interconnect (PCI) bus standard or theUniversal Serial Bus (USB) standard, for example. In some embodiments,the function of I/O interface 1206 may be split into two or moreseparate components, such as a north bridge and a south bridge, forexample. Also, in some embodiments some or all of the functionality ofI/O interface 1206, such as an interface to memory 1204, may beincorporated directly into processor 1202.

Network interface 1208 may be configured to allow data to be exchangedbetween computing device 1200 and other devices 1216 attached to anetwork or networks 1218, such as other computer systems or devices asdescribed above with reference to FIGS. 1-11, for example. In variousembodiments, network interface 1208 may support communication via anysuitable wired or wireless general data networks, such as types ofEthernet network, for example. Additionally, network interface 1208 maysupport communication via telecommunications/telephony networks such asanalog voice networks or digital fiber communications networks, viastorage area networks such as Fibre Channel SANs, or via any othersuitable type of network and/or protocol.

In some embodiments, main memory 1204 may be one embodiment of acomputer-accessible medium configured to store program instructions anddata as described above with reference to FIGS. 1-11 for implementingembodiments of the corresponding methods, systems, and apparatus.However, in other embodiments, program instructions and/or data may bereceived, sent or stored upon different types of computer-accessiblemedia. Generally speaking, a computer-accessible medium may includenon-transitory storage media or memory media such as magnetic or opticalmedia, e.g., disk or DVD/CD coupled to computing device 1200 via I/Ointerface 1206. A non-transitory computer-accessible storage medium mayalso include any volatile or non-volatile media such as RAM (e.g. SDRAM,DDR SDRAM, RDRAM, SRAM, etc.), ROM, etc., that may be included in someembodiments of computing device 1200 as main memory 1204 or another typeof memory. Further, a computer-accessible medium may includetransmission media or signals such as electrical, electromagnetic, ordigital signals, conveyed via a communication medium such as a networkand/or a wireless link, such as may be implemented via network interface1208. Portions or all of multiple computing devices such as thatillustrated in FIG. 12 may be used to implement the describedfunctionality in various embodiments; for example, software componentsrunning on a variety of different devices and servers may collaborate toprovide the functionality. In some embodiments, portions of thedescribed functionality may be implemented using storage devices,network devices, or special-purpose computer systems, in addition to orinstead of being implemented using general-purpose computer systems. Theterm “computing device”, as used herein, refers to at least all thesetypes of devices, and is not limited to these types of devices.

Conclusion

Various embodiments may further include receiving, sending or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a computer-accessible medium. Generally speaking, acomputer-accessible medium may include storage media or memory mediasuch as magnetic or optical media, e.g., disk or DVD/CD-ROM, volatile ornon-volatile media such as RAM (e.g. SDRAM, DDR, RDRAM, SRAM, etc.),ROM, etc., as well as transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as network and/or a wireless link.

The various methods as illustrated in the figures and described hereinrepresent example embodiments of methods. The methods may be implementedin software, hardware, or a combination thereof. The order of method maybe changed, and various elements may be added, reordered, combined,omitted, modified, etc.

Various modifications and changes may be made as would be obvious to aperson skilled in the art having the benefit of this disclosure. It isintended to embrace all such modifications and changes and, accordingly,the above description to be regarded in an illustrative rather than arestrictive sense.

1.-6. (canceled)
 7. A vehicle, comprising: a window that at leastpartially encompasses an interior of the vehicle; one or more lightingmodules to illuminate at least a portion of the window to facilitatedetection of one or more volume defects in the at least a portion of thewindow; an imaging system including one or more sensor devices to:obtain first data by imaging the at least a portion of the window whilethe at least a portion of the window is illuminated by the one or morelighting modules; and one or more processors to: evaluate, based atleast in part on the first data, one or more parameters thatcharacterize the one or more volume defects in the at least a portion ofthe window to produce parameter evaluation data, wherein the one or moreparameters include a distribution of the one or more volume defects withrespect to the at least a portion of the window; and designate, base atleast in part on the parameter evaluation data, at least one of a repairstatus of at least the portion of the window or a replace status of thewindow, wherein: designation of the repair status indicates a suggestionto repair at least the portion of the window; and designation of thereplace status indicates a suggestion to replace at least the portion ofthe window.
 8. The vehicle of claim 7, wherein the one or more lightingmodules include at least one of: a first edge lighting module that islocated at a top edge of the window and that provides light in adownward direction through the window; or a second edge lighting modulethat is located at a bottom edge of the window and that provides lightin an upward direction through the window.
 9. The vehicle of claim 7,wherein the one or more lighting modules include at least one of: afirst graze lighting module to provide light that is incident on a firstside of the window; or a second graze lighting module to provide lightthat is incident on a second side of the window.
 10. The vehicle ofclaim 7, wherein: the imaging system is further to: obtain second databy imaging at least a portion of a scene that is exterior to thevehicle, wherein the second data includes an altered representation ofthe at least a portion of the scene based at least in part on one ormore image altering effects induced by the one or more volume defects inthe at least a portion of the window; and the one or more processors arefurther to: produce, based at least in part on the first data and thesecond data, a corrected image of the at least a portion of the scene,wherein to produce the corrected image the one or more processorsperform image processing to compensate for the one or more imagealtering effects induced by the one or more volume defects.
 11. Thevehicle of claim 10, wherein the one or more sensor devices of theimaging system include: a first sensor device to obtain the first data;and a second sensor device to obtain the second data.
 12. The vehicle ofclaim 11, wherein: the at least a portion of the window is a firstportion of the window; the one or more lighting modules illuminate asecond portion of the window to facilitate detection of one or morevolume defects in the second portion of the window; the one or moresensor devices of the imaging system further include: a third sensordevice to obtain third data by imaging the second portion of the windowwhile the second portion of the window is illuminated by the one or morelighting modules, wherein at least a portion of the third datacorresponds to a third image that includes a representation of the oneor more volume defects in the second portion of the window; and toproduce the corrected image, the one or more processors perform imageprocessing based at least in part on the third data obtained by thethird sensor device.
 13. The vehicle of claim 10, wherein: the vehicleis an autonomous or partially-autonomous vehicle; and the one or moreprocessors are further to: determine to modify a state of operation ofthe autonomous or partially-autonomous vehicle based at least in part onone or more of: the first data; or the corrected image.
 14. The vehicleof claim 10, wherein: the vehicle is an autonomous orpartially-autonomous vehicle; and the one or more processors are furtherto: assign one or more degrees of confidence to one or more of: thefirst data; the second data; or the corrected image; and determine tomodify a state of operation of the autonomous or partially-autonomousvehicle based at least in part on the one or more degrees of confidence.15. The vehicle of claim 7, further comprising: a window cleaning systemconfigured to spot clean the window; wherein the one or more processorsare further to: cause, based at least in part on the parameterevaluation data, the window cleaning system to spot clean one or moreparticular areas of the at least a portion of the window.
 16. (canceled)17. The vehicle of claim 7, wherein the one or more sensor devicesinclude at least one of: a camera; a radar device; or a light detectionand ranging (LIDAR) device. 18.-20. (canceled)
 21. A system, comprising:a window; one or more lighting modules to illuminate at least a portionof the window to facilitate detection of one or more volume defects inthe at least a portion of the window; an imaging system including one ormore sensor devices to: obtain first data by imaging the at least aportion of the window while the at least a portion of the window isilluminated by the one or more lighting modules; and one or moreprocessors to: evaluate, based at least in part on the first data, oneor more parameters that characterize the one or more volume defects inthe at least a portion of the window to produce parameter evaluationdata, wherein the one or more parameters include a distribution of theone or more volume defects with respect to the at least a portion of thewindow; and designate, base at least in part on the parameter evaluationdata, at least one of a repair status of at least the portion of thewindow or a replace status of the window, wherein: designation of therepair status indicates a suggestion to repair at least the portion ofthe window; and designation of the replace status indicates a suggestionto replace at least the portion of the window.
 22. The system of claim21, wherein the one or more lighting modules include at least one of: afirst edge lighting module that is located at a top edge of the windowand that provides light in a downward direction through the window; or asecond edge lighting module that is located at a bottom edge of thewindow and that provides light in an upward direction through thewindow.
 23. The system of claim 21, wherein the one or more lightingmodules include at least one of: a first graze lighting module toprovide light that is incident on a first side of the window; or asecond graze lighting module to provide light that is incident on asecond side of the window.
 24. The system of claim 21, wherein: theimaging system is further to: obtain second data by imaging at least aportion of a scene, wherein the second data includes an alteredrepresentation of the at least a portion of the scene based at least inpart on one or more image altering effects induced by the one or morevolume defects in the at least a portion of the window; and the one ormore processors are further to: produce, based at least in part on thefirst data and the second data, a corrected image of the at least aportion of the scene, wherein to produce the corrected image the one ormore processors perform image processing to compensate for the one ormore image altering effects induced by the one or more volume defects.25. The system of claim 24, wherein the one or more sensor devices ofthe imaging system include: a first sensor device to obtain the firstdata; and a second sensor device to obtain the second data.
 26. Thesystem of claim 25, wherein: the at least a portion of the window is afirst portion of the window; the one or more lighting modules illuminatea second portion of the window to facilitate detection of one or morevolume defects in the second portion of the window; the one or moresensor devices of the imaging system further include: a third sensordevice to obtain third data by imaging the second portion of the windowwhile the second portion of the window is illuminated by the one or morelighting modules, wherein at least a portion of the third datacorresponds to a third image that includes a representation of the oneor more volume defects in the second portion of the window; and toproduce the corrected image, the one or more processors perform imageprocessing based at least in part on the third data obtained by thethird sensor device.
 27. The system of claim 21, wherein the one or moresensor devices include at least one of: a camera; a radar device; or alight detection and ranging (LIDAR) device.
 28. A method, comprising:illuminating, using one or more lighting modules of a vehicle, at leasta portion of a window such that one or more defects associated with theat least a portion of the window are illuminated to facilitate detectionof the one or more volume defects; imaging, using one or more sensordevices of an imaging system of the vehicle, the at least a portion ofthe window while the at least a portion of the window is illuminated bythe one or more lighting modules, to obtain first data; evaluating,based at least in part on the first data, one or more parameters thatcharacterize one or more volume defects in the at least a portion of thewindow to produce parameter evaluation data, wherein the one or moreparameters include a distribution of the one or more volume defects withrespect to the at least a portion of the window; and designating, baseat least in part on the parameter evaluation data, at least one of arepair status of at least the portion of the window or a replace statusof the window, wherein: designation of the repair status indicates asuggestion to repair at least the portion of the window; and designationof the replace status indicates a suggestion to replace at least theportion of the window.
 29. The method of claim 28, further comprising:imaging, using at least one of the one or more sensor devices, at leasta portion of a scene that is exterior to the vehicle, to obtain seconddata that includes an altered representation of the at least a portionof the scene based at least in part on one or more image alteringeffects induced by the one or more volume defects in the at least aportion of the window; and producing, based at least in part on thefirst data and the second data, a corrected image of the at least aportion of the scene, wherein the producing the corrected imagecomprises: performing, using one or more processors, image processing tocompensate for the one or more image altering effects induced by the oneor more volume defects.
 30. The method of claim 29, further comprising:assigning one or more degrees of confidence to one or more of: the firstdata; the second data; or the corrected image; and modifying a state ofoperation of the vehicle based at least in part on the one or moredegrees of confidence.